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      "execution_count": 1,
      "metadata": {
        "id": "Bw3IBSWA-aNz"
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      "source": [
        "import tensorflow_datasets as tfds"
      ]
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        "datasets = tfds.list_builders()"
      ],
      "metadata": {
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      },
      "execution_count": 3,
      "outputs": []
    },
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      "source": [
        "print(f\"TFDS contains {len(datasets)} datasets\")"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "9WqQW-qK-gLP",
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      },
      "execution_count": 4,
      "outputs": [
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          "text": [
            "TFDS contains 224 datasets\n"
          ]
        }
      ]
    },
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      "source": [
        "data, info  = tfds.load(name=\"mnist\", as_supervised=True, split=['train', 'test'], with_info=True)"
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          "text": [
            "WARNING:absl:Dataset mnist is hosted on GCS. It will automatically be downloaded to your\n",
            "local data directory. If you'd instead prefer to read directly from our public\n",
            "GCS bucket (recommended if you're running on GCP), you can instead pass\n",
            "`try_gcs=True` to `tfds.load` or set `data_dir=gs://tfds-data/datasets`.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mDownloading and preparing dataset mnist/3.0.1 (download: 11.06 MiB, generated: 21.00 MiB, total: 32.06 MiB) to /root/tensorflow_datasets/mnist/3.0.1...\u001b[0m\n"
          ]
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            "\n",
            "\u001b[1mDataset mnist downloaded and prepared to /root/tensorflow_datasets/mnist/3.0.1. Subsequent calls will reuse this data.\u001b[0m\n"
          ]
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    {
      "cell_type": "code",
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        "print(info)"
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      "metadata": {
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        },
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        "outputId": "3adf4e73-7d75-4a3d-e996-75f3f44312a2"
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            "tfds.core.DatasetInfo(\n",
            "    name='mnist',\n",
            "    version=3.0.1,\n",
            "    description='The MNIST database of handwritten digits.',\n",
            "    homepage='http://yann.lecun.com/exdb/mnist/',\n",
            "    features=FeaturesDict({\n",
            "        'image': Image(shape=(28, 28, 1), dtype=tf.uint8),\n",
            "        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),\n",
            "    }),\n",
            "    total_num_examples=70000,\n",
            "    splits={\n",
            "        'test': 10000,\n",
            "        'train': 60000,\n",
            "    },\n",
            "    supervised_keys=('image', 'label'),\n",
            "    citation=\"\"\"@article{lecun2010mnist,\n",
            "      title={MNIST handwritten digit database},\n",
            "      author={LeCun, Yann and Cortes, Corinna and Burges, CJ},\n",
            "      journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},\n",
            "      volume={2},\n",
            "      year={2010}\n",
            "    }\"\"\",\n",
            "    redistribution_info=,\n",
            ")\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_train = data[1].take(1)\n",
        "for sample, lable in data_train:\n",
        "  print(sample.shape)\n",
        "  print(lable)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dgvw4BFaNTPY",
        "outputId": "5113778f-1791-4096-a176-2e648bc8bd68"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(28, 28, 1)\n",
            "tf.Tensor(2, shape=(), dtype=int64)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = tfds.show_examples(data[0], info)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "V-Tj3uW2Jnpl",
        "outputId": "fd0fdac9-c2ab-4088-c882-e357b8afebef"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = tfds.show_examples(data[1], info)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "ShJRwbkuVHIP",
        "outputId": "b106075a-5319-4ef0-d742-24d8fa4396d0"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x648 with 9 Axes>"
            ],
            "image/png": 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7m3Pux2LjKr+3KQh+tVXS1d77wyR1lDTQOXdYfJJzrkBSX0lPJqJXJLXy3h8paamkGyTJe/9/kpo4536TjcUDIc65apIelPSfkg6TdF4J+7q+pI7e+zmJ6B5Jw733zSWtl9Qvkf+vpMHlvnAgdVdI+rikLwb2tpxzR0vaMza1yu9tCoIE7/033vv3E7/+QUUbrHFgaldJ73vvtybmztj+a0lvS2pSbO4Lks4tv1UDSbWXtMx7/7n3foukCZJ6BOb9XtJLkuSccyra5xMTXxsr6XeS5L3/SdJy51z78l44kIxzromk0yQ9tpNpO/Z24ppqkv4i6Y/FJ7G3KQiCnHPNJLWV9E7gy50kzS/h0r6SphcbvyepcybXBpRSY0lfFRuvVLjQLb6v60vaUKzQjV/Dvka+uF9F/8e+bSdz4p/ZgyQ9773/JjC3Su9tCoIY51xtSc9KGuK93xiY0lDS2sB1N6noxw7/KBavkdSoPNYJZFhwX5eAfY2cc86dLmmN976kP6Btt2NvO+caSeopaUQJc6v03i7tSYWVmnNuVxUVA//w3k8qYdpmSTVj110k6XRJJ/roSU81E/OBXFklqWmxcZNEFld8X38nqZ5zriDxlCB+Dfsa+aCTpO7OuVNVtCf3cM6N8973is0rvrfbSmouaVnRT8ZUyzm3LNErI1Xxvc0TgoTEz01HS/rYe3/fTqZ+rKINtf26U1T0yKp74mdQxbWQtCjTawVKYZ6kgxN/a6C6inpang/M27GvE0XtLElnJ752oaQpxeayr5Fz3vsbvPdNvPfNVLSvZwaKASm6t6d67xt475slrvupWDEgVfG9TUHwq06Sekvq6pxbkPjfqYF50yUV/+srIyXVkfRK4pqHi33tBElTy23FQBKJP+EPkvSyij4Yn/beLw5MnSrp+GLj6yRd5ZxbpqKegtHFvtZJRX+7BqgI4nt7Z6r03uZdBmlwzk2W9Efv/ac7mVND0mxJxxZrzgLylnNurqTTvfcbdjKnraSrvPe9s7cyoGzY26mhIEiDc66lpP2K/73WwJyDJTX23r+etYUBZeCc6yBps/d+4U7mdJP0qfd+edYWBpQRezs1FAQAAIAeAgAAQEEAAABEQQAAAERBAAAAREEAAABEQQAAAERBAAAAREEAAABEQQAAAERBAAAAREEAAABEQQAAAERBAAAAREEAAABEQQAAAERBAAAAREEAAABEQQAAAERBAAAAREEAAABEQQAAAERBAAAAREEAAABEQQAAAERBAAAAREEAAABEQQAAAERBAAAAJBWUZrJzzpfXQlBxee9drtdQFuxrlGCd936fXC+iLNjbCCnpM5snBAAQtiLXCwCyiYIAAABQEAAAAAoCAAAgCgIAACAKAgAAIAoCAAAgCgIAACAKAgAAIAoCAAAgCgIAACAKAgAAIAoCAACgUr7tEACAqqRbt24mGzhwoMm6d+9usmHDhkXG119/feYWVg54QgAAACgIAAAABQEAABAFAQAAEE2FAIAqqmHDhiY7+eSTI+P77rvPzKlbt67JvPcmGzJkSGT86aefmjmjR49Ous5s4QkBAACgIAAAABQEAABAFAQAAEA0FQIAKpnatWubrFevXibr27evyY466qiMraNatWqRcZ06dTJ27/LAEwIAAEBBAAAAKAgAAIAoCAAAgCQXOl2pxMnOpT4ZQfvtt5/Jzj//fJO1adMm6b1GjBhhsvfeey+9hZWB995l/ZtmEPsaJZjvvT8614soi6q6t+fMmWOyTp06mcw5+9EV///EwsJCM2f48OEmC70Sef369ZHxAQccYBebAyV9ZvOEAAAAUBAAAAAKAgAAIA4myqj4IRSS9Mc//jEyvuqqq8yc0M+x9tprr6Tfr1GjRibr1q1b0uuQe4ccckhkfP/995s5jRs3NlmoRyR07YcffliG1QH5K/57Z8qUKWZO06ZN077/999/HxlfcsklZs5zzz1nstCbE8ePH5/2OnKBJwQAAICCAAAAUBAAAABREAAAAHEwUdpat25tsv/5n/8xWY8ePSLjsWPHmjm33nqryb766iuTPfHEE5Fx165dzZxQY0tIgwYNTLZ69eqUro3jYKLSO+644yLj119/Pe17bd261WRLly6NjOfOnZv2/adNm2ayzZs3R8ZnnXWWmZNuQ9Xy5ctNtmLFirTuVUYcTJRjBQW27/2BBx6IjC+77LK07x/6nL3yyisj48mTJ6d9/3zFwUQAAKBEFAQAAICCAAAAUBAAAADRVJiSjh07muzxxx832UEHHWSyeMPLmDFjzJxt27altI74yXXTp083c/r06WOyW265xWShk+yGDh2a0jriaCosvRo1akTGoX1x3nnnZWs5eeWHH34w2bvvvmuyk046qbyXQlNhFsVPIJSkwYMHm6wsTYRxodNlqwKaCgEAQIkoCAAAAAUBAACgIAAAAOL1xym5+uqrTdayZUuTxU8llKTnn38+Y+vYtGlTZBx6/fG8efNMdvPNN5vsvvvuy9i6UHqFhYWRcd++fc2c2267zWQnn3yyyTZu3GiyCy64IDIuy+tgUxE6ITPULFu7du2k96pTp47JPvjgg/QWhgojlabsVL3yyismGzFiRFr3qkp4QgAAACgIAAAABQEAABAFAQAAEE2FRrNmzUwWerXrqFGjTPbCCy9kbB2/+c1vTBZvitlzzz1TWte9995rsp9//rkMq0Omhf57LFmyJKUsJHTyYXlq0aKFyQ499FCTTZo0yWS77BL9c8m///1vM2fRokVlWB0qglDzdio2bNhgsuuvv95kCxYsSOv+VQlPCAAAAAUBAACgIAAAAKKHwGjQoIHJnLMvhpo9e7bJQm+OLCiI/ivu37+/mdO1a1eTnXLKKSZbtmxZZHz22WebOZMnTzYZUN4+/fRTk919990mi/cLSPb3zbXXXmvmjB07tgyrQ0Vw4IEHpnVd/BAuiX6BdPGEAAAAUBAAAAAKAgAAIAoCAAAgmgqNNm3apDRv3bp1Jgu9mWvgwIGR8eGHH27mrF+/3mT33HOPyeIHE3333XdJ1wlkw/HHH2+yM888M6Vr42/eHD58eCaWhDx2ww03mCx0GFsq3njjjbTX0apVK5N17tw56XWht45279496XVTpkwx2TnnnGOyLVu2JL1XeeAJAQAAoCAAAAAUBAAAQBQEAABANBUa9evXT2neiy++aLL4qYSS9MEHH0TGffr0MXMmTJhgssLCwpTWAeTCxRdfHBk/+uijKV33/fffm+zOO+/MyJqQn0Kfi6EGwtBJryH3339/ZLxp0yYzp3Xr1iarU6eOyZ566imThU6rTUUq6w81HtasWdNkNBUCAICcoSAAAAAUBAAAgIIAAACIpkL9x3/8R2R83XXXpXRdqOmjR48eJnvppZfSWxiQJ5o0aWKyK664Iq17XXrppSYLndSJymP33Xc32X//93+nfb+NGzdGxqHXx48bN85ke++9t8lCr7ZPpTkw1PS96667miz0uu98VrFWCwAAygUFAQAAoCAAAAAUBAAAQFWsqbBfv34me+SRRyLjZcuWmTlr1qwx2VFHHWWyUFMJUNE9++yzJgu9Njbu4YcfNtlzzz2XkTWh6rr55puz+v2ef/55k4X29qhRo0zWtGnTcllTeeEJAQAAoCAAAAAUBAAAQJWkh2C//fYz2bBhw0x26qmnmizeV/Dkk0+aOaE3c40ZM8ZkI0eONNm8efMi49WrV5s5QL449thjTRZ6c1zcW2+9ZbL+/ftnZE1AeZk6darJHnzwwcg49JbE0047zWSNGjVK+v2WLFlisq1btya9Llt4QgAAACgIAAAABQEAABAFAQAAUAVsKiwosEteunSpyUJvsQq9Feu9995L+j1DhxWNGDHCZBMmTDBZhw4dIuMpU6Yk/X5ANhx99NEme+2110xWvXp1k40fPz4yHjBgQOYWBpTRL7/8YrL77rvPZH/+859NFn8DbuhzPVWffPJJZNy9e3cz56effkr7/pnGEwIAAEBBAAAAKAgAAIAoCAAAgCpAU2H8DYJz5swxcwoLC00WbwyRpAULFmRsXfXr109p3rp16zL2PYF07bKLrf2feOIJk4UaCN955x2TxZsIN2zYUIbVAZmV6n585plnTNatW7eMrePqq6+OjD/77LOM3bs88IQAAABQEAAAAAoCAAAgCgIAAKA8ayrce++9TXb77bdHxvGT/yTpmGOOMVkmGwhr1Khhst69e5tsy5YtJgudoghkW+h13YceeqjJNm7caLJrrrnGZDQRIlWhU2PL2z777GOy6667zmShZttt27Ylvf/ixYtN9uSTT5rslVdeSXqvfMITAgAAQEEAAAAoCAAAgPKshyB0iE+tWrUi4++//97MqVmzpslCb0UMadOmTWTctGlTMyf0lqzQvNtuu81ka9euTWkdQCYNHDgwMr7gggtSuu5vf/ubyebOnZuRNaFq+vHHH03WpUsXkz3wwAMma9u2bbmsaTvvfdI5oT6wM844w2QrVqzIyJpyiScEAACAggAAAFAQAAAAURAAAABJLpWmih2TnUt9cobEm/5Gjx5t5mSy8SR0KMXs2bNNdvnll5ssdFhFVeC9z/7JIxmUi32dSbvttpvJ4gcHhd5iOGPGDJP17NnTZKHDiqqI+d77o3O9iLKoSHt7//33N9kLL7xgssMPPzxj3/ONN94w2YQJEyLjV1991cxZtmxZxtaQCyV9ZvOEAAAAUBAAAAAKAgAAIAoCAACgCtBUGNegQQOTnXjiiWnfL3661JIlS8yc0AmK+BVNhbl15513muzGG2+MjENNUEceeaTJNm/enLmFVXw0FaJSoqkQAACUiIIAAABQEAAAAAoCAACgCthUiPxDU2H21K9f32TLly83We3atSPjk08+2cwJnVSICJoKUSnRVAgAAEpEQQAAACgIAAAABQEAAJBUkOsFAEjdGWecYbJ4A2FI6DWvAFAcTwgAAAAFAQAAoCAAAACihwCoUFLpFwi55pprTHb77beXdTkAKhGeEAAAAAoCAABAQQAAAERBAAAAxNsOkQG87RCVFG87RKXE2w4BAECJKAgAAAAFAQAAoCAAAAAq/UmF6yStKI+FoMLaP9cLyAD2NULY26iMStzXpfpbBgAAoHLiRwYAAICCAAAAUBAAAABREEQ45650zi12zi1yzo13ztUsYd79zrkuiV+f6Jx73zm3wDk31znXPJEPcs71zeb6gTjnXMvE3tz+v43OuSElzB3inLsg8eunil2z3Dm3IJEf4Zx7PIv/CECJnHP1nHMTnXNLnHMfO+f+XwnzduztxHhw4prFzrlhiazK722aChOcc40lzZV0mPd+s3PuaUnTvPePx+bVlzTVe98xMV4qqYf3/mPn3ABJ7b33Fznnakl603vfNrv/JECYc66apFWSOnjvV8S+ViDpfUntvPdbY1/7q6R/ee9vS4xfldTXe/9ldlYOhDnnxkp6w3v/mHOuuqRa3vsNsTmRve2cO0HSTZJO894XOuf29d6vScyt0nubJwRRBZJ2S2ygWpK+Dsz5vaSXio29pD0Sv667/Rrv/U+Sljvn2pffcoFSOVHSZ/FiIKGrpPcDxYCT9AdJ44vFL0g6t9xWCaTAOVdXUhdJoyXJe78lXgwkxPd2f0l3e+8LE9etKTa3Su9tCoIE7/0qSfdK+lLSNyr6E9GMwNROkuYXG18saZpzbqWk3pLuLva19yR1Lp8VA6V2rqL/x15cfF9v11nSt977T4tl7GvkgwMkrZU0xjn3gXPuMefc7oF58b3dQlJn59w7zrnZzrnfFvtald7bFAQJzrk9JfVQ0SZrJGl351yvwNSGKtqE210p6VTvfRNJYyTdV+xraxL3AnIq8Ti1u6RnSpgS39fbnSdbRLCvkQ8KJLWT9FDiR7ObJF0fmBff2wWS9pLUUdK1kp5OPAmTqvjepiD41UmSvvDer/Xe/yJpkqRjAvM2S6opSc65fSS19t6/k/jaU7FraibmA7n2nyp6bPptCV/fsa+3S/zo7CwV7evi2NfIByslrSz2+TtRRQVCXHxvr5Q0yRd5V9I2SXsnvlal9zYFwa++lNTROVcrUS2eKOnjwLyPJTVP/Hq9pLrOuRaJcbfYNS0kLYmWZPQAABKOSURBVCqn9QKlEfqTfnHF9/V2J0la4r1fGcvZ18g57/1qSV8551omohMlfRSYGt/bz0k6QZISn93VVXTEs1TF9zYFQUKiypyoom7U/1PRv5tHAlOnSjo+cc1WSZdIetY596GKegiuLTa3k6RXym/VQHKJn6t2U9FTr5JMV1GDVnEl9RycoKLfB0CuDZb0D+fcQkltJN0VmBPf2/8r6UDn3CJJEyRd6H/963ZVem/z1w7T4JybK+n0Ejpat89pK+kq733v7K0MSJ9zbrKkP8YaCONzakiaLenY+N9IAPIVezs1FARpcM51kLTZe79wJ3O6SfrUe788awsDyiDx6HU/7/2cncw5WFJj7/3rWVsYUEbs7dRQEAAAAHoIAAAABQEAABAFAQAAEAUBAAAQBQEAABAFAQAAEAUBAAAQBQEAABAFAQAAEAUBAAAQBQEAABAFAQAAEAUBAAAQBQEAABAFAQAAEAUBAAAQBQEAABAFAQAAEAUBAAAQBQEAABAFAQAAEAUBAAAQBQEAABAFAQAAEAUBAAAQBQEAABAFAQAAEAUBAACQVFCayc45X14LQcXlvXe5XkNZsK9RgnXe+31yvYiyYG8jpKTPbJ4QAEDYilwvAMgmCgIAAEBBAAAAKAgAAIAoCAAAgCgIAACAKAgAAIAoCAAAgCgIAACAKAgAAIAoCAAAgCgIAACAKAgAAIAoCAAAgCgIAACAKAgAAIAoCAAAgCgIAACApIJcLwBAxVO7du3I+NFHHzVzzj33XJO9/fbbJjv55JMj440bN5ZxdajqqlevbrIaNWqkdO1JJ50UGd9yyy1mzhFHHJHSveLX3nHHHSldlys8IQAAABQEAACAggAAAIiCAAAAqBI3FRYU2H+0iy++2GQHH3xw0nv9+OOPJnvsscdMtmbNGpMVFhYmvT+Qzw455BCTTZs2LTJu1qyZmeO9N1mHDh1M1rt378j4wQcfLOUKkU+qVasWGbds2dLMufTSS8t1DUceeaTJOnfubDLnnMlC+zadOVJ4v+cznhAAAAAKAgAAQEEAAABEQQAAAFSJmwr/9Kc/pZSlItR4ctNNN5ls1qxZJnv11Vd3Opak+fPnp7UuINMaNmxospdfftlkTZs2jYwfeeQRM+e2224z2bJly0wWagBGxbXvvvtGxgsXLszRSrJr8+bNJps0aVIOVpI+nhAAAAAKAgAAQEEAAAAkuVQPWJAk51zqk7PovPPOM9m4ceNMVpp/1uLSPbwi5JdffjHZ+++/b7KnnnrKZLNnz46MP/zww7TWkGnee/svqALJ131d3nbbbTeTjRkzxmR/+MMfTPbSSy9Fxj179jRzNm3aZLKnn37aZKNGjYqMX3vtNbvY3JjvvT8614soi1zs7XgfyldffZXtJaQsk5/t11xzjcnuv//+tO5V3kr6zOYJAQAAoCAAAAAUBAAAQBQEAABAlaSpcPHixSYLvaEtH5oKy3Kv+FsXx48fb+b0798/rXWVBU2FFVNor4TeNPjFF1+YrHXr1pFx6I2gIaG3Iq5atSoyDjXe5ghNhWmoUaNGZDxy5Egzp0+fPmnf/4MPPoiMQ5/1oYbZkFQ+j0MHDg0dOtRk//jHP0y2du3alNaRbTQVAgCAElEQAAAACgIAAEBBAAAAVAGbCkeMGGGyAQMGmGyXXWyts23btrS+Z+i6r7/+2mQTJkww2bRp0yLj+GmDktSoUSOTnXPOOSa78sorI+MmTZqYOfEGLUk688wzTbZgwQKTbd261WSpoKkw/x19tO2Ne/PNN032ww8/mOzkk082WRV5QydNhRkQavA76qij0r5f/LMrtBcPOuiglO4Vair8+eefI+NBgwaZOaETPSsSmgoBAECJKAgAAAAFAQAAoCAAAACSCnK9gGTq1KkTGXfp0sXMCTVGhhoBQw1TY8eOjYzbtWtn5syYMcNkt99+u11smkINisOHDzfZN998ExmHTsaKv3pUkt5++22TDRw40GTx19Ci8rj88stNtuuuu5rsn//8p8mqSAMhyknopL+5c+emfb/4Xm7atGna9wo1Useb1OP/H1GZ8YQAAABQEAAAAAoCAAAgCgIAAKAKcFLhhRdeGBmPHj06petCJ1ANGTLEZKGTDyuKUFNh6ITDkKlTp5qsR48eaa2DkwrzT/v27SPjt956y8z57LPPTBY60TDUjFtFcFJhjg0ePNhk99xzT2RcvXr1tO9/0UUXmWzcuHFp36+i4KRCAABQIgoCAABAQQAAAPLsYKLQW/9GjhyZ1r1Ch/089thjad0rX61evTrta0MHGKFiCv0M9fHHH4+MQ2///Pvf/26yUL9AzZo1k37PjRs3JlsmsFOhw9KGDRtmstCBWumqCv0CpcETAgAAQEEAAAAoCAAAgCgIAACA8qypsGvXriarVatWWvcKNUeF3rpVkcXfBCmFD2QKmTNnTqaXgxw566yzTHbIIYckva5FixYm++KLL0xWUGA/JqpVqxYZ//zzz2bOhAkTTHbLLbeY7JdfftnpOlH5nHnmmSYbNGiQyTLZQBhyww03pHXd5MmTTbZkyZKyLifneEIAAAAoCAAAAAUBAAAQBQEAAFCeNRW2bdvWZKV5G2Nxjz76aFmXk3dOP/30yLhfv35mTqr/vtL994r8E3pDYSp69eplsi1btpgs9Hsp3kQYfyupJF1//fUme+mll0xGg2vl1rx5c5NNnDgxByux7rrrLpNt27Yt6XV33HGHyZ5++mmTDR06NDJetmxZKVaXfTwhAAAAFAQAAICCAAAAiIIAAAAoz5oKMyl0SlpFd+qpp2bsXvne3IKw0Mmdp512Wlr3WrFihcluvPFGk40fPz7pvZ599lmTvfXWWyYbNWqUyY466qjI+Keffkr6/VCx5UtTc6iBMN219ezZ02Tt27ePjEOnii5evNhkW7duTWsNZcUTAgAAQEEAAAAoCAAAgCgIAACA8qypsF27dmld9/7775vsm2++Ketycurmm282WehkwlQsXbrUZJWx6bIqOOOMM0zWsmXLpNetWrXKZN26dTNZus2m8+fPT2leaK21a9eOjGkqrFzWrFljsptuuslkffr0MVmoiXaPPfaIjGvUqGHmbNq0yWTr1q0zWeh18XvvvXdkXLduXTMnVfvvv39kHPp90rFjR5O99957aX/PsuAJAQAAoCAAAAAUBAAAQHnWQ3DccceZLJVDIir629JatWplsksvvdRkBQXR/1yhn3+F3lYXeqvdv/71r9IsEXmiYcOGaV03ffp0k3E4FbJh48aNJrvnnntSyho0aGCyZs2aRcb16tUzc1avXm2yBQsW7GyZO7Rp0yYy/u1vf2vmDBkyxGSp9PKEhA4DO+ecc0z2yy+/pHX/0uAJAQAAoCAAAAAUBAAAQBQEAABAedZUGGogTKWpMF/enJWKUAPh1KlTTbbffvuZLP7PGWogDDW7hA5uQtUyceLEcr1/qPkrJPRmtx9++CHTy0ElEWoODGWZFG8+DDUjhj6zX3/9dZMdeOCBSb9f9+7dTbbXXnuZ7Ntvv016r7LiCQEAAKAgAAAAFAQAAEAUBAAAQHnWVPjpp5+arHnz5jlYSebE31oYOoEw1ECYisGDB5vsscceS+teqBi+++67tK6bOXNmRtcRPzVz7NixKV3397//3WSbN2/OyJqQH+JvHzzrrLPMnMsuu8xkX375pckeeOABk2X7TYBHHnmkya699lqTpdJAGLJy5UqThRrGs4EnBAAAgIIAAABQEAAAAFEQAAAA5VlTYej0pyuuuCIHK0nu9NNPN9mf/vQnk7Vt2zYyjjdjSamftDhgwIDImAbCqmfGjBlpXbfHHnuY7Pvvv0/p2l133dVk8Uax448/3sxZtWqVyUJNYqhcrrnmmsj41ltvTem6Tp06mSz0Ofv5559HxgsXLjRzpk2bltL3vOGGG0wW/zxu2rSpmRM6STBd559/vsnWr1+fsfuXBk8IAAAABQEAAKAgAAAAyrMegh9//NFkzrmk14V+PpqqWrVqRcb169c3c4YOHWqyfv36pfX9Qv88oUMoOHQIIaGf+8+ePdtkxx13XGQc/7muJN14440mS6VfQJLGjx8fGYd+75522mkmKywsNBkql3333Tdj96pTp47JWrduvdOxJPXu3Tul+4c+jzP59tz4oUMjR440c+bNm5ex71dWPCEAAAAUBAAAgIIAAACIggAAAEhypWmgcM5lrtsiINSMsmjRosg41QMhnn322ZTmNWnSJDLu0KGDmZPJxpNXX33VZPfcc4/JZs2aldb9c8F7n7zzM4+V974ub126dDHZ9OnTI+PQGwXjv7ckaffddzfZUUcdZbJ4E2H37t3NnNdff91kFcx87/3RuV5EWeRib8cPnxo4cGC2l5CyTH62P//88yaLv+029HsuF0r6zOYJAQAAoCAAAAAUBAAAQBQEAABAedZUGNKiRYvIuH///mbOxRdfbLL4CYRS+s0iqTaezJw502TxJsJhw4altYZ8RlNh/mnUqFFk/MQTT5g5Xbt2NdmGDRtM9swzz5hsxIgRkXG+NEtlGE2FaahRo0ZkHHrDa8gf/vAHkx144IFJr7vssstMtueee6b0PefMmWOyN998MzIO/Z54+OGHTRY6hXPr1q0prSPbaCoEAAAloiAAAAAUBAAAgIIAAACoAjQVpqJhw4YmCzVMtWnTJq37b9q0yWShVxGvWbPGZKFXG1c2NBWikqKpEJUSTYUAAKBEFAQAAICCAAAAUBAAAABVkqZC5BZNhaikaCpEpURTIQAAKBEFAQAAoCAAAAAUBAAAQBQEAABAFAQAAEAUBAAAQBQEAABAFAQAAEAUBAAAQBQEAABAFAQAAEAUBAAAQFJBKeevk7SiPBaCCmv/XC8gA9jXCGFvozIqcV+X6vXHAACgcuJHBgAAgIIAAABQEAAAAFEQRDjnTnHOfeKcW+acu34n8+53znVJ/PoA59w7iWuecs5VT+SDnHN9s7V2oCTOuXrOuYnOuSXOuY+dc/+vhHlDnHMXFBsPTlyz2Dk3LJEd4Zx7PEtLB0rknKvpnHvXOfdhYo/eupO5xT+zRyeuWZj4fVE7kVf5z2yaChOcc9UkLZXUTdJKSfMknee9/yg2r76kqd77jonx05Imee8nOOcelvSh9/4h51wtSW9679tm9R8EiHHOjZX0hvf+sUTBWst7vyE2p0DS+5Laee+3OudOkHSTpNO894XOuX2992sSc1+V1Nd7/2WW/1GAHZxzTtLu3vsfnXO7Spor6Qrv/duxefHP7D289xsTv75P0hrv/d18ZvOEoLj2kpZ57z/33m+RNEFSj8C830t6SdqxIbtKmpj42lhJv5Mk7/1PkpY759qX98KBkjjn6krqImm0JHnvt8SLgYSukt733m9NjPtLutt7X5i4bk2xuS9IOrf8Vg0k54v8mBjumvhf6E+4Oz6zE9dtLwacpN22X8NnNgVBcY0lfVVsvDKRxXWSND/x6/qSNhT7EI1f856kzhleJ1AaB0haK2mMc+4D59xjzrndA/OK72tJaiGpc+LHYbOdc78t9jX2NfKCc66ac26BpDWSXvHevxOYFt/bcs6NkbRa0iGSRhT7UpXe2xQEpddQRR+wqVgjqVE5rgVIpkBSO0kPJR6FbpIU6o+J7+sCSXtJ6ijpWklPJ/5EJbGvkSe89//23reR1ERSe+dcq8A085ntve+joj38saRzin2pSu9tCoJfrZLUtNi4SSKL2yypZuLX30mql/j5a+iamon5QK6slLSy2J+cJqqoQIgrvq+3Xzcp8Vj2XUnbJO2d+Br7Gnkl8WOwWZJOCXw5vre3X/NvFf1o+PfF4iq9tykIfjVP0sGJvzVQXUU/I30+MO9jSc2lop9hqWgTnp342oWSphSb20LSonJbMZCE9361pK+ccy0T0YmSPgpM3bGvE56TdIIkOedaSKquomNwJfY18oBzbh/nXL3Er3dTUUP4ksDUHXvbFdnxa0ndY9dU6b1NQZCQ6AMYJOllFW2gp733iwNTp0o6vtj4OklXOeeWqainYHSxr3WS9Eq5LBhI3WBJ/3DOLZTURtJdgTnTVdR8uN3/SjrQObdIRX+KutD/+leSTlDR7wMglxpKmpXY1/NU1EPwYmBe8c9sJ2msc+7/JP1f4h63FZtbpT+z+WuHaXDOzZV0egnd2tvntJV0lfe+d/ZWBqTPOTdZ0h+995/uZE4NSbMlHVusmRbIa3xmp4aCIA3OuQ6SNnvvF+5kTjdJn3rvl2dtYUAZJH6ssJ/3fs5O5hwsqbH3/vWsLQwoIz6zU0NBAAAA6CEAAAAUBAAAQBQEAABAFAQAAEAUBAAAQNL/BwbOhv+SEbocAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "Y_Rlxqn7VN9E"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}